PM2.5 Estimation in Day/Night-Time from Himawari-8 Infrared Bands via a Deep Learning Neural Network

Abstract

Satellite-based PM2.5 estimation is an effective means to achieve large-scale and long-term PM2.5 monitoring and investigation. Currently, most of methods retrieve PM2.5 from satellite-derived aerosol optical depth (AOD) or top-of-atmosphere reflectance (TOAR) during daytime. A few algorithms are also developed to retrieve nighttime PM2.5 from the satellite day–night band and the accuracy is greatly limited by moonlight and artificial light sources. In this study, we utilize the properties of absorption pollutants in infrared spectrum to estimate PM2.5 concentrations from satellite infrared data, thus achieve the PM2.5 estimation in both day and night. Himawari-8 infrared bands data are used for PM2.5 estimation by a specifically designed neural network and loss function. Quantitative results show the satellite derived PM2.5 concentrations correlates with ground-based data well with R2 of 0.79 and RMSE of 15.43 μg · m−3 for hourly PM2.5 estimation. Spatiotemporal distributions of model-estimated PM2.5 over China are also analyzed, and exhibit a highly consistent with ground-based measurements. Dust storms, heavy air pollution and fire smoke events are examined to further demonstrate the efficacy of our model. Our method not only circumvents the intermediate retrievals of AOD, but also enables consistent estimation of PM2.5 concentrations during daytime and nighttime in real-time monitoring.